Medical data processing apparatus and medical image diagnostic apparatus

ABSTRACT

According to one embodiment, a medical data processing apparatus includes processing circuitry. The processing circuitry acquires medical data, and generates an imaging parameter by inputting the medical data to a trained model, the imaging parameter being a parameter of a medical image diagnostic apparatus with respect to the medical data, the trained model being trained to generate an imaging parameter of the medical image diagnostic apparatus based on medical data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2019-147717, filed Aug. 9, 2019, theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a medical dataprocessing apparatus and a medical image diagnostic apparatus.

BACKGROUND

Conventionally, in a medical image diagnostic apparatus, when a userexecutes medical imaging, for example, an imaging parameter at the timeof shipment, an imaging parameter used in the past, or an imagingparameter described in a textbook or a paper is used. Furthermore, in acase where a medical image and an imaging parameter are associated witheach other, a user can select a medical image of a desired aspect, anduse an imaging parameter associated with the selected medical image.

However, in some cases, a medical image may not be associated with animaging parameter. In such case, it is difficult for the user toestimate the parameter used for the imaging.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a configuration of a magnetic resonance imaging apparatusaccording to a first embodiment.

FIG. 2 shows a first example of input/output of a trained model used ina generating function of FIG. 1, and an MR imaging apparatus of anoutput destination.

FIG. 3 is a flowchart for explaining a series of events including MRimaging parameter estimation processing in the first embodiment relatingto FIG. 2.

FIG. 4 shows an example of a display screen of a reference MR image andan estimated MR imaging parameter.

FIG. 5 shows an example of a display screen of a reference MR image andan output MR image.

FIG. 6 shows a second example of input/output of a trained model used inthe generating function of FIG. 1.

FIG. 7 shows a third example of input/output of a trained model used inthe generating function of FIG. 1.

FIG. 8 shows a fourth example of input/output of a trained model used inthe generating function of FIG. 1.

FIG. 9 shows a fifth example of input/output of a trained model used inthe generating function of FIG. 1, and a specific example of processingrelating to input.

FIG. 10 shows a sixth example of input/output of a trained model used inthe generating function of FIG. 1.

FIG. 11 shows a configuration of an X-ray computed tomography apparatusaccording to a second embodiment.

FIG. 12 shows a first example of input/output of a trained model used ina generating function of FIG. 11, and a CT imaging apparatus of anoutput destination.

FIG. 13 shows a second example of input/output of a trained model usedin a generating function of FIG. 12, a CT imaging apparatus of an outputdestination, and a specific example of processing relating to input.

FIG. 14 is a flowchart for explaining a series of events including CTimaging parameter estimation processing in the second embodimentrelating to FIG. 13.

FIG. 15 shows a configuration of an ultrasound diagnostic apparatusaccording to a third embodiment.

FIG. 16 shows a first example of input/output of a trained model used ina generating function of FIG. 15, and an ultrasound diagnostic apparatusof an output destination.

FIG. 17 shows a second example of input/output of a trained model usedin a generating function of FIG. 16, an ultrasound diagnostic apparatusof an output destination, and a specific example of processing relatingto input.

FIG. 18 is a flowchart for explaining a series of events includingultrasound imaging parameter estimation processing in the thirdembodiment relating to FIG. 17.

DETAILED DESCRIPTION

In general, according to one embodiment, a medical data processingapparatus includes processing circuitry. The processing circuitryacquires medical data, and generates an imaging parameter by inputtingthe medical data to a trained model, the imaging parameter being aparameter of a medical image diagnostic apparatus with respect to themedical data, the trained model being trained to generate an imagingparameter of the medical image diagnostic apparatus based on medicaldata.

Hereinafter, embodiments of the medical data processing apparatus andthe medical image diagnostic apparatus will be explained in detail withreference to the drawings.

The medical data processing apparatus according to the presentembodiments is a computer or a processor that processes medical dataacquired by a medical apparatus, etc., or medical data stored in anexternal storage apparatus, etc. As the medical apparatus according tothe present embodiments, the medical image diagnostic apparatus orbiological information measuring equipment can be used. The medicalimage diagnostic apparatus acquires a medical image by performingmedical imaging on a subject by various imaging principles. Examples ofthe medical image diagnostic apparatus include a magnetic resonanceimaging apparatus, an X-ray computed tomography apparatus, an ultrasounddiagnostic apparatus, a nuclear medicine diagnostic apparatus, an X-raydiagnostic apparatus, an optical coherence tomography apparatus, anoptical ultrasonic apparatus, and an endoscope. The biologicalinformation measuring equipment acquires waveform data relating tobiological information of a subject by various measurement principles.Examples of the biological information measuring equipment include anautomatic analysis apparatus, an electrocardiograph, a spirometer, asphygmomanometer, and a pulse oximeter.

The medical data according to the present embodiments includes medicalimage data. The medical image data is acquired from, for example, amedical image diagnostic apparatus or a picture archiving andcommunication system (PACS). However, the acquisition source may be anysource. As the medical image data, there are magnetic resonance (MR)image data, map image data based on MR imaging, CT image data,ultrasound image data, X-ray image data, and the like. The map imagedata will be described later. The medical image data may be a digitalimaging and communication in medicine (DICOM) format or a non-DICOMformat.

The medical data according to the present embodiments may includemedical image-like image data. The medical image-like image data is, forexample, image data obtained by photographing or reading a medical imageprinted on paper or recorded on film. Specifically, the medicalimage-like image data is image data obtained by clipping medical imagedata, image data acquired by photographing a medical image printed onpaper by a camera, or image data captured by an optical scanner, etc. Asthe medical image-like image data, there are MR image-like image data,CT image-like image data, ultrasound image-like image data, and X-rayimage-like image data, etc.

The medical data according to the present embodiments may includesupplementary data relating to the image data. The supplementary datais, for example, a known imaging parameter, DICOM data, and label data.The imaging parameter is, for example, a parameter relating to imagingof the medical image diagnostic apparatus when acquiring medical imagedata. The DICOM data is, for example, tag data relating to an imagingparameter that is attached to medical image data in DICOM format. Thelabel data is, for example, data in one-hot vector format, in which thepresence of an element is corresponded to “zero” or “one”.

The imaging parameter may include information relating to the medicalimage diagnostic apparatus. The information relating to the medicalimage diagnostic apparatus is, for example, information on staticmagnetic field strength (for example, 1.5 T, 3 T, and 7 T) in the caseof the magnetic resonance imaging apparatus, information on the numberof rows of multi-slices (for example, 64 rows and 320 rows) in the caseof the X-ray computed tomography apparatus, and information on the typeof probe (for example, a convex probe, a sector probe, a linear probe,and a 3D probe) in the case of the ultrasound diagnostic apparatus.

The medical data processing apparatus according to the presentembodiments may be a computer or a processor mounted on the medicalapparatus, or may be a computer or a processor that is separate from themedical apparatus. For the purpose of providing detailed explanations,the medical data processing apparatus according to a first embodiment isassumed as a computer mounted on the magnetic resonance imagingapparatus, the medical data processing apparatus according to a secondembodiment is assumed as a computer mounted on the X-ray computedtomography apparatus, and the medical data processing apparatusaccording to a third embodiment is assumed as a computer mounted on theultrasound diagnostic apparatus.

First Embodiment

FIG. 1 shows a configuration of a magnetic resonance imaging apparatusaccording to a first embodiment. As shown in FIG. 1, a magneticresonance imaging apparatus 1 includes a gantry 10, a couch 30, agradient field power supply 21, transmitting circuitry 23, receivingcircuitry 25, a couch motor 27, sequence control circuitry 29, and amedical data processing apparatus 50.

The gantry 10 includes a static field magnet 41 and a gradient fieldcoil 43. The static field magnet 41 and the gradient field coil 43 areaccommodated in the housing of the gantry 10. The housing of the gantry10 is formed with a bore having a hollow shape. A transmitting coil 45and a receiving coil 47 are disposed in the bore of the gantry 10.

The static field magnet 41 has a hollow substantially cylindrical shapeand generates a static magnetic field inside a substantially cylindricalinterior. Examples of the static field magnet 41 used include apermanent magnet, a superconducting magnet or a normal conductingmagnet. Here, a central axis of the static field magnet 41 is defined asa Z axis, an axis vertically perpendicular to the Z axis is defined as aY axis, and an axis horizontally perpendicular to the Z axis is definedas an X axis. The X axis, the Y axis and the Z axis constitute anorthogonal three-dimensional coordinate system.

The gradient field coil 43 is a coil unit attached to the inside of thestatic field magnet 41 and formed in a hollow substantially cylindricalshape. The gradient field coil 43 receives supply of a current from thegradient field power supply 21 to generate a gradient field. Morespecifically, the gradient field coil 43 has three coils correspondingto the X axis, the Y axis, and the Z axis orthogonal to each other. Thethree coils form a gradient field in which the magnetic field strengthchanges along the X axis, the Y axis, and the Z axis respectively. Thegradient fields respectively along the X axis, the Y axis, and the Zaxis are combined to form slice selection gradient fields Gs, phaseencoding gradient fields Gp, and frequency encoding gradient fields Grthat are orthogonal to each other in arbitrary directions. The sliceselection gradient fields Gs are used to determine the imaging crosssection (slice) arbitrarily. The phase encoding gradient fields Gp areused to vary the phase of the magnetic resonance signal (hereinafterreferred to as the MR signal) according to a spatial position. Thefrequency encoding gradient fields Gr are used to vary the frequency ofthe MR signal according to the spatial position. It should be noted thatin the following description, it is assumed that the direction ofgradient of the slice selection gradient fields Gs corresponds to the Zaxis, the direction of gradient of the phase encoding gradient fields Gpcorresponds to the Y axis, and the direction of gradient of thefrequency encoding gradient fields Gr corresponds to the X axis.

The gradient field power supply 21 supplies a current to the gradientfield coil 43 in accordance with a sequence control signal from thesequence control circuitry 29. The gradient field power supply 21supplies a current to the gradient field coil 43 and causes the gradientfield coil 43 to generate a gradient field along each of the X axis, Yaxis, and Z axis. The gradient field is superimposed on the staticmagnetic field formed by the static field magnet 41 and applied to asubject P.

The transmitting coil 45 is disposed, for example, inside the gradientfield coil 43, and receives supply of a current from the transmittingcircuitry 23 to generate a high frequency magnetic field pulse(hereinafter referred to as an RF magnetic field pulse).

The transmitting circuitry 23 supplies a current to the transmittingcoil 45 in order to apply an RF magnetic field pulse for exciting atarget proton in the subject P to the subject P via the transmittingcoil 45. The RF magnetic field pulse oscillates at a resonance frequencyspecific to the target proton to excite the target proton. An MR signalis generated from the excited target proton and detected by thereceiving coil 47. The transmitting coil 45 is, for example, awhole-body coil (WB coil). The whole-body coil may be used as atransmitting and receiving coil.

The receiving coil 47 receives the MR signal emitted from the targetproton present in the subject P under an action of the RF magnetic fieldpulse. The receiving coil 47 has a plurality of receiving coil elementscapable of receiving the MR signal. The received MR signal is suppliedto the receiving circuitry 25 via wire or wireless means. A1though notshown in FIG. 1, the receiving coil 47 has a plurality of receivingchannels implemented in parallel. The receiving channels each includereceiving coil elements that receive the MR signal, an amplifier thatamplifies the MR signal, and the like. The MR signal is output for eachreceiving channel. The total number of the receiving channels and thetotal number of the receiving coil elements may be the same, or thetotal number of the receiving channels may be larger or smaller than thetotal number of the receiving coil elements.

The receiving circuitry 25 receives the MR signal generated from theexcited target proton via the receiving coil 47. The receiving circuitry25 processes the received MR signal to generate a digital MR signal. Thedigital MR signal can be expressed in k-space defined by a spatialfrequency. Therefore, hereinafter, the digital MR signal is referred toas k-space data. The k-space data is supplied to the medical dataprocessing apparatus 50 via wire or wireless.

It should be noted that the transmitting coil 45 and the receiving coil47 described above are merely examples. Instead of the transmitting coil45 and the receiving coil 47, a transmitting and receiving coil having atransmitting function and a receiving function may be used. A1so, thetransmitting coil 45, the receiving coil 47, and the transmitting andreceiving coil may be combined.

The couch 30 is installed adjacent to the gantry 10. The couch 30 has atable top 33 and a base 31. The subject P is placed on the table top 33.The base 31 slidably supports the table top 33 respectively along the Xaxis, the Y axis, and the Z axis. The couch motor 27 is accommodated inthe base 31. The couch motor 27 moves the table top 33 under the controlof the sequence control circuitry 29. The couch motor 27 may, forexample, include any motor such as a servo motor or a stepping motor.

The sequence control circuitry 29 has a processor such as a centralprocessing unit (CPU) or a micro processing unit (MPU) and a memory suchas a read only memory (ROM) or a random access memory (RAM) as hardwareresources. The sequence control circuitry 29 synchronously controls thegradient field power supply 21, the transmitting circuitry 23, and thereceiving circuitry 25 based on an imaging protocol determined byprocessing circuitry 51, executes MR imaging on the subject P inaccordance with a pulse sequence corresponding to the imaging protocol,and acquires the k-space data relating to the subject P.

As shown in FIG. 1, the medical data processing apparatus 50 is acomputer having processing circuitry 51, a memory 52, a display 53, aninput interface 54, and a communication interface 55.

The processing circuitry 51 includes, as hardware resources, a processorsuch as a CPU. The processing circuitry 51 functions as the core of themagnetic resonance imaging apparatus 1. For example, by executingvarious programs, the processing circuitry 51 realizes an acquisitionfunction 511, a generating function 512, an output function 513, adisplay control function 514, and a training function 515. It should benoted that, although not shown, by executing various programs, theprocessing circuitry 51 realizes an imaging protocol setting function,an image reconstruction function, and an image processing function.

In the imaging protocol setting function, the processing circuitry 51sets an imaging protocol relating to MR imaging by a user instructionvia the input interface 54 or automatically. The imaging protocol is aset of various MR imaging parameters related to MR imaging. The MRimaging parameter will be described later.

In the image reconstruction function, the processing circuitry 51reconstructs an MR image based on k-space data acquired by variousscans. A reconstruction method is not limited in particular.

In the image processing function, the processing circuitry 51 performsvarious types of image processing on the MR image. For example, theprocessing circuitry 51 performs image processing such as volumerendering, surface rendering, pixel value projection processing,multi-planer reconstruction (MPR) processing, and curved MPR (CPR)processing.

In the acquisition function 511, the processing circuitry 51 acquiresmedical data from the PACS, etc. by the user's instruction. In thepresent embodiment, the processing circuitry 51 acquires, for example,MR image data, map image data, and MR image-like image data.

Furthermore, the processing circuitry 51 may acquire supplementary data.

In the generating function 512, the processing circuitry 51 applies atrained model to medical data and generates an imaging parameter of amedical image diagnostic apparatus relating to the medical data. Inother words, the processing circuitry 51 generates an imaging parameterby inputting the medical data to a trained model, the imaging parameterbeing a parameter of a medical image diagnostic apparatus with respectto the medical data, the trained model being trained to generate animaging parameter of the medical image diagnostic apparatus based onmedical data. The trained model is, for example, a machine learningmodel that is trained based on medical data and an imaging parameter ofa medical image diagnostic apparatus relating to the medical data,prepared in advance. It should be noted that, in the present embodiment,in the case where medical image data is included in the medical data,the medical image data is associated with the medical image diagnosticapparatus. Furthermore, in the case where medical image-like image datais included in the medical data, the image data is associated with amedical image apparatus that acquired the medical image.

In the present embodiment, for example, the processing circuitry 51applies a trained model to the MR image data and generates an MR imagingparameter relating to the MR image data. In other words, the processingcircuitry 51 generates an MR imaging parameter by inputting the MR imagedata to a trained model, the MR imaging parameter being a parameter of amagnetic resonance imaging apparatus with respect to the MR image data,the trained model being trained to generate an MR imaging parameter ofthe magnetic resonance imaging apparatus based on an MR image data.Here, the trained model is, for example, a machine learning model thatis trained based on MR image data and an MR imaging parameter (an MRimaging parameter of a magnetic resonance imaging apparatus) that isused when acquiring k-space data corresponding to the MR image data,prepared in advance.

The machine learning model according to the present embodiment isassumed, typically, to be a deep neural network (DNN), which is amultilayered network model simulating a neural circuit of a brain of aliving creature. The DNN includes a parameterized synthesis functiondefined by a combination of a plurality of adjustable functions andparameters.

In the output function 513, the processing circuitry 51 outputs theimaging parameter of the medical image diagnostic apparatus generated bythe generating function 512 to an imaging apparatus. The term imagingapparatus refers to the units and circuitry performing medical imagingin the medical image diagnostic apparatus. The processing circuitry 51may convert the imaging parameter into an imaging parameter inaccordance with an imaging apparatus of the output destination. Theimaging apparatus may be identical to the medical image diagnosticapparatus. In the present embodiment, for example, the processingcircuitry 51 outputs the MR imaging parameter of the magnetic resonanceimaging apparatus generated by the generating function 512 to an MRimaging apparatus.

In the display control function 514, the processing circuitry 51displays various types of information on the display 53. For example,the processing circuitry 51 displays the imaging parameter of themedical image diagnostic apparatus, etc. output by the output function513 on the display 53. In the present embodiment, the processingcircuitry 51 displays, for example, a reference MR image used as inputdata, the MR imaging parameter of the magnetic resonance imagingapparatus output by the output function 513, and an MR image (output MRimage) of the subject P imaged by using the MR imaging parameter on thedisplay 53.

In the training function 515, the processing circuitry 51 generates atrained model that is trained based on medical data and an imagingparameter of a medical image diagnostic apparatus relating to themedical data, prepared in advance. For example, the processing circuitry51 generates the trained model by supervised training using the medicaldata and the imaging parameter of the medical image diagnostic apparatusrelating to the medical data as supervising data. When training,reinforcement training may also be used in combination.

In the present embodiment, for example, the processing circuitry 51generates a machine learning model that is trained based on MR imagedata and an MR imaging parameter relating to the MR image data, preparedin advance. For example, the processing circuitry 51 generates thetrained model by supervised training that uses the MR image data and theMR imaging parameter relating to the MR image data as supervising data.

In summary, the medical data processing apparatus acquires medical data,and generates an imaging parameter by inputting the medical data to atrained model, the imaging parameter being a parameter of a medicalimage diagnostic apparatus with respect to the medical data, the trainedmodel being trained to generate an imaging parameter of the medicalimage diagnostic apparatus based on medical data. Therefore, the presentmedical data processing apparatus is capable of estimating an imagingparameter from medical data of which an imaging parameter is unknown sothat, for example, a user may set an optimal imaging parameter of themedical image diagnostic apparatus. The medical data includes at leastthe image data.

The memory 52 is a storage apparatus such as a hard disk drive (HDD), asolid state drive (SSD), an integrated circuitry storage apparatus orthe like that stores various information. The memory 52 may also be adrive apparatus or the like that reads and writes various informationfrom and to a portable storage medium such as a CD-ROM drive, a DVDdrive, a flash memory, and the like. For example, the memory 52 storesk-space data, MR image data, map image data, various programs, and thelike.

The display 53 displays various types of information by the displaycontrol function 514. For example, the display 53 displays the imagingparameter of the medical image diagnostic apparatus output by the outputfunction 513. In the present embodiment, the display 53 displays, forexample, the reference MR image, the MR imaging parameter, the output MRimage, and the like. Examples of displays 53 that can be used asappropriate include a CRT display, a liquid crystal display, an organicEL display, an LED display, a plasma display, or any other display knownin the art.

The input interface 54 includes an input device that receives variouscommands from the user. Examples of the input device that can be usedare a keyboard, a mouse, various switches, a touch screen, a touch pad,and the like. It should be noted that the input device is not limited tothose having physical operation parts such as a mouse and keyboard. Forexample, the input interface 54 could also include electrical signalprocessing circuitry that receives an electrical signal corresponding toan input operation from an external input device provided separatelyfrom the magnetic resonance imaging apparatus 1, and outputs thereceived electrical signal to various types of circuitry.

The communication interface 55 is an interface connecting the magneticresonance imaging apparatus 1 with a workstation, PACS, a hospitalinformation system (HIS), a radiology information system (RIS), and thelike via a local area network (LAN) or the like. A network IF transmitsand receives various types of information to and from the connectedworkstation, PACS, HIS, and RIS.

It should be noted that the above configuration is merely an example,and the present invention is not limited thereto. For example, thesequence control circuitry 29 may be incorporated into the medical dataprocessing apparatus 50. A1so, the sequence control circuitry 29 and theprocessing circuitry 51 may be mounted on the same substrate. Thesequence control circuitry 29, the gradient field power supply 21, thetransmitting circuitry 23 and the receiving circuitry 25 may be mountedon a single control apparatus different from the medical data processingapparatus 50 or may be distributed and mounted on a plurality ofapparatuses.

Hereinafter, an operation example of the magnetic resonance imagingapparatus 1 according to the present embodiment will be explained.

FIG. 2 shows a first example of input/output of the trained model usedin the generating function of FIG. 1, and an imaging apparatus of anoutput destination. The processing circuitry 51 acquires MR image databy the acquisition function 511. By the generating function 512, theprocessing circuitry 51 applies a trained model 60 to the MR image dataand generates an MR imaging parameter relating to the MR image data. Bythe output function 513, the processing circuitry 51 outputs the MRimaging parameter to an MR imaging apparatus 61.

The MR imaging parameter is a parameter relating to imaging of themagnetic resonance imaging apparatus when acquiring the MR image data.Furthermore, the MR imaging parameter may include information relatingto the magnetic resonance imaging apparatus.

Examples of MR imaging parameters include types of acquisition systems,types of acquisition methods, a time parameter, a flip angle, an imagingcross-section, types of reconstructions, FOV, a matrix size, a slicethickness, the number of phase encoding steps, a scan option, and thelike.

The types of acquisition systems include, for example, information onstatic magnetic field strength and information on an acquisition coil(for example, a head coil and a body coil). The types of acquisitionmethods include, for example, information on types of sequences (forexample, spin echo (SE), fast spin echo (FSE), echo planar (EP),inversion recovery (IR), gradient echo (GRE), and balanced steady statefree precession (bSSFP)). The time parameter includes, for example,information on a time parameter (for example, an echo time (TE) and arepeating time (TR)) that sets the characteristic of the pulse sequence.The types of reconstructions include, for example, information on areconstruction method attributable to an imaging technique (for example,parallel imaging (PI) and compressed sensing (CS)) and information on areconstruction method using deep learning (deep learning reconstruction(DLR)). The matrix size includes, for example, information on the numberof samples in an XY direction of an image and information on spatialresolution. The scan option includes, for example, information onsettings relating to an individual sequence (for example, spatialpresaturation (SP), fat saturation (FS), pre-inversion recovery(PreIR)), and number of shots).

FIG. 3 is a flowchart for explaining a series of events including MRimaging parameter estimation processing in the first embodiment relatingto FIG. 2. For example, the flowchart of FIG. 3 starts by the processingcircuitry 51 executing an MR imaging parameter estimation program, whichis triggered by an instruction to activate an application relating tothe MR imaging parameter estimation processing input by a user.

(Step SA1)

When the MR imaging parameter estimation program is executed, theprocessing circuitry 51 executes the acquisition function 511. When theacquisition function 511 is executed, the processing circuitry 51acquires MR image data assigned by the user.

(Step SA2)

After acquiring the MR image data, the processing circuitry 51 executesthe generating function 512. When the generating function 512 isexecuted, the processing circuitry 51 applies a trained model to the MRimage data and generates an MR imaging parameter.

(Step SA3)

After generating the MR imaging parameter, the processing circuitry 51executes the display control function 514. When the display controlfunction 514 is executed, the processing circuitry 51 displays theacquired MR image data (reference MR image) and the generated MR imagingparameter (estimated MR imaging parameter) on the display 53.

FIG. 4 shows an example of a display screen of the reference MR imageand the estimated MR imaging parameter. A display screen D1 includes anMR image P1 assigned by the user to estimate a parameter and a table T1that lists the estimated MR imaging parameters.

(Step SA4)

After the display screen D1 is displayed, the processing circuitry 51presents a determination on whether or not there is a modification inthe estimated MR imaging parameter to the user. Specifically, theprocessing circuitry 51 displays a GUI (not shown) on the display screenD1 so that the parameter can be modified, and performs the determinationbased on whether or not an input relating to a modification instructionmade by the user has been received. In the case where a modification hasnot been made by the user, the processing proceeds to step SA6, and inthe case where a modification has been made by the user, the processingproceeds to step SA5.

(Step SA5)

The processing circuitry 51 modifies the MR imaging parameter based onthe modification instruction from the user. After step SA5, theprocessing proceeds to step SA6.

Step SA3, step SA4, and step SA5 may be omitted.

(Step SA6)

After the processing relating to the modification of the MR imagingparameter, the processing circuitry 51 executes the output function 513.When the output function 513 is executed, the processing circuitry 51outputs the MR imaging parameter to an MR imaging apparatus. It shouldbe noted that the MR imaging apparatus is explained as being identicalto the magnetic resonance imaging apparatus 1.

(Step SA7)

After receiving the MR imaging parameter, the processing circuitry 51executes the imaging protocol setting function. When the imagingprotocol setting function is executed, the processing circuitry 51 setsthe imaging protocol based on the received MR imaging parameter, andoutputs it to the sequence control circuitry 29. The sequence controlcircuitry 29 executes MR imaging and generates k-space data.

(Step SA8)

After the k-space data is generated, the processing circuitry 51executes the image reconstruction function. When the imagereconstruction function is executed, the processing circuitry 51reconstructs the generated k-space data and generates MR image data.

(Step SA9)

After the MR image data is generated, the processing circuitry 51executes the display control function 514. When the display controlfunction 514 is executed, the processing circuitry 51 displays the MRimage data (reference MR image) acquired in step SA1 and the generatedMR image data (output MR image) on the display 53, and ends the MRimaging parameter estimation processing.

FIG. 5 shows an example of a display screen of the reference MR imageand the output MR image. A display screen D2 includes the MR image P1and an MR image P2 obtained by the imaging based on the estimated MRimaging parameter.

In the flowchart of FIG. 3, the MR imaging parameter estimationprocessing is explained as including imaging processing; however, it isnot limited thereto. In the case where the MR imaging parameterestimation processing does not include the imaging processing, the MRimaging parameter estimation processing includes at least step SA1, stepSA2, and step SA6. Furthermore, the display of the MR imaging parameterin step SA2 may be omitted.

In the above, the medical image data input to the trained model has beenexplained as being the MR image data; however, it is not limitedthereto. In the following FIG. 6 to FIG. 10, cases in which the medicalimage data to be input to the trained model is other than the MR imagedata will be explained. It should be noted that an output from thetrained model is an MR imaging parameter in either of the cases.Furthermore, in FIG. 6 to FIG. 10, the MR imaging apparatus, which is anoutput destination of the MR imaging parameter, is omitted.

FIG. 6 shows a second example of input/output of the trained model usedin the generating function of FIG. 1. By the generating function 512,the processing circuitry 51 applies a trained model 62 to map imagedata, and generates an MR imaging parameter relating to the map imagedata. A map image is, for example, an image (for example, a tumor-likemap, a T1 map, a T2 map, and an ADC map) calculated from a plurality ofMR images. In order to acquire the map image, it is necessary to performimaging by a plurality of pulse sequences. Therefore, the trained model62 is trained to output the MR imaging parameter corresponding to thenumber of pulse sequences. For example, in the case of the tumor-likemap, the trained model 62 is trained to output a set of MR imagingparameters necessary for each of T1 weighting (T1W), T2 weighting (T2W),fluid-attenuated inversion recovery (FLAIR), and diffusion weighting(DWI).

FIG. 7 shows a third example of input/output of the trained model usedin the generating function of FIG. 1. By the generating function 512,the processing circuitry 51 applies a trained model 63 to MR image-likeimage data, and generates an MR imaging parameter relating to the MRimage-like image data.

The above-mentioned trained model 60, trained model 62, and trainedmodel 63 are explained as being trained, respectively, using a singletype of input data; however, the trained models are not limited thereto.For example, the trained model may be trained using a plurality of typesof input data.

FIG. 8 shows a fourth example of input/output of the trained model usedin the generating function of FIG. 1. By the generating function 512,the processing circuitry 51 applies a trained model 64 to the MR imagedata, the map image data, or the MR image-like image data, andsupplementary data (label data) that specifies the type of the pluralityof image data, and generates an MR imaging parameter relating to thespecified image data. Specifically, the processing circuitry 51 switchesamong a plurality of models corresponding to the type of image databased on the label data. For example, the trained model 64 comprises thetrained model 60, the trained model 62, and the trained model 63,respectively. Based on the label data, the processing circuitry 51switches among the trained model 60, the trained model 62, and thetrained model 63. It should be noted that, for example, these models maybe divided into three independent models, or may have a weight switchedper model by a software switch, etc. The label data described above canserve to function as, for example, the software switch.

The label data of FIG. 8 is data in which the type of input image datais digitized. For example, the label data may indicate a case where theMR image data is input as [1, 0, 0], a case where the map image data isinput as [0, 1, 0], and a case where the MR image-like image data isinput as [0, 0, 1]. That is, the label data associates a position of avalue of a vector with the type of the image data, and expresses thepresence of the type by the value of the vector.

FIG. 9 shows a fifth example of input/output of a trained model used inthe generating function of FIG. 1, and a specific example of processingrelating to input. In the example of FIG. 9, an MR imaging parameter ofan MR image printed on an article A1 can be estimated. In the samemanner as the trained model 64 of FIG. 8, a trained model 67 of FIG. 9responds to input of a plurality of types of image data.

Specifically, the user reads the article A1 with an optical scanner 65,and acquires MR image-like image data P3. As preprocessing 66, theprocessing circuitry 51 generates label data from the acquired imagedata. P3. The label data here is the same as the label data of FIG. 8.By the generating function 512, the processing circuitry 51 applies thetrained model 67 to the image data P3 and the label data, and generatesan MR imaging parameter relating to the image data P3.

FIG. 10 shows a sixth example of input/output of a trained model used inthe generating function of FIG. 1. By the acquisition function 511, theprocessing circuitry 51 acquires MR image data and supplementary data(DICOM data) relating to the MR image data. By the generating function512, the processing circuitry 51 applies a trained model 68 to the MRimage data and the DICOM data relating to the MR image data, andgenerates an MR imaging parameter relating to the MR image data. In thecase where there is a difference between the input DICOM data and thegenerated MR imaging parameter, when outputting the MR imaging parameterto a subsequent MR imaging apparatus, the processing circuitry 51 maychange the value of the known DICOM data to the value of the MR imagingparameter.

Preferably, the type of DICOM tag to be included in the DICOM data ofFIG. 10 is predetermined at a design stage. Accordingly, when inputtingthe DICOM data to the trained model 68, the processing circuitry 51 mayattach label data in a one-hot vector format indicating the presence ofthe DICOM tag included in the DICOM data to the DICOM data.

As explained above, the magnetic resonance imaging apparatus accordingto the first embodiment acquires MR image data, and generates an MRimaging parameter by inputting the MR image data to a trained model, theMR imaging parameter being a parameter of the magnetic resonance imagingapparatus with respect to the MR image data, the trained model beingtrained to generate an MR imaging parameter of the magnetic resonanceimaging apparatus based on an MR image data. Therefore, the magneticresonance imaging apparatus according to the present embodiment iscapable of estimating an MR imaging parameter used for MR imaging fromMR image data of which the MR imaging parameter is unknown so that, forexample, a user may set an optimal MR imaging parameter.

Second Embodiment

In the first embodiment, the medical data processing apparatus isexplained as being a computer mounted on the magnetic resonance imagingapparatus. On the other hand, in the second embodiment, the medical dataprocessing apparatus will be explained as being a computer mounted on anX-ray computed tomography apparatus. In the following explanation,constituent elements having functions almost identical to those in thefirst embodiment will be given identical symbols, and will be providedwith explanations only when necessary.

FIG. 11 shows a configuration of an X-ray computed tomography apparatusaccording to the second embodiment. In FIG. 11, a plurality of gantries10-2 are illustrated for convenience of explanation; however, typically,the X-ray computed tomography apparatus is equipped with one gantry10-2.

As shown in FIG. 11, the X-ray computed tomography apparatus 1-2includes a gantry 10-2, a couch 30-2, and a medical data processingapparatus (console) 50-2. The gantry 10-2 is a scanning apparatus havinga configuration for performing X-ray CT imaging on a subject P. Thecouch 30-2 is a carrier device on which the subject P to be the X-ray CTimaging target is placed, and is for positioning the subject P. Themedical data processing apparatus 50-2 is a computer for controlling thegantry 10-2. For example, the gantry 10-2 and the couch 30-2 areinstalled in an examination room, and the medical data processingapparatus 50-2 is installed in a control room adjacent to theexamination room. The gantry 10-2, the couch 30-2, and the medical dataprocessing apparatus 50-2 are connected by cable or wirelessly in acommunicable manner with each other.

As shown in FIG. 11, the gantry 10-2 includes an X-ray tube 11, an X-raydetector 12, a rotation frame 13, an X-ray high-voltage apparatus 14, acontroller 15, a wedge filter 16, a collimator 17, and a dataacquisition system (DAS) 18.

The X-ray tube 11 generates an X-ray. Specifically, the X-ray tube 11includes a negative electrode for generating thermoelectrons, a positiveelectrode for generating X-rays by receiving the thermoelectrons flyingfrom the negative electrode, and a vacuum tube for maintaining thenegative electrode and the positive electrode. The X-ray tube 11 isconnected to the X-ray high-voltage apparatus 14 via a high-voltagecable. A filament current is supplied to the negative electrode by theX-ray high-voltage apparatus 14. By supplying the filament current, thethermoelectrons are generated from the negative electrode. A tubevoltage is applied between the negative electrode and the positiveelectrode by the X-ray high-voltage apparatus 14. By applying the tubevoltage, thermoelectrons fly toward the positive electrode from thenegative electrode and collide with the positive electrode, therebygenerating an X-ray. The generated X-ray is irradiated on the subject P.The thermoelectrons flying from the negative electrode to the positiveelectrode causes a tube current to flow.

The X-ray detector 12 detects the X-ray generated from the X-ray tube 11passing through the subject P, and outputs an electric signalcorresponding to the detected X-ray dose to the DAS 18. The X-raydetector 12 has a structure in which a plurality of X-ray detectingelement rows, in each row of which a plurality of X-ray detectingelements are arranged in a channel direction, are arranged in a slicedirection (a row direction). The X-ray detector 12 is, for example, anindirect conversion-type detector having a grid, a scintillator array,and a photosensor array. The scintillator array has a plurality ofscintillators. Scintillators output light of a light quantity inaccordance with an incident X-ray dose. The grid has an X-ray shieldingplate that is arranged on an X-ray incident surface side of thescintillator array and absorbs scattered X-rays. The grid may bereferred to as a collimator (a one-dimensional collimator or atwo-dimensional collimator). The photosensor array converts the lightfrom the scintillator into an electric signal in accordance with thelight quantity thereof. As a photosensor, for example, a photodiode isused.

The rotation frame 13 is an annular frame for supporting the X-ray tube11 and the X-ray detector 12 rotatably about a rotational axis Z.Specifically, the rotation frame 13 supports the X-ray tube 11 and theX-ray detector 12 opposite each other. The rotation frame 13 isrotatably supported about the rotational axis Z by a fixed frame (notshown). When the rotation frame 13 is rotated about the rotational axisZ by the controller 15, the X-ray tube 11 and the X-ray detector 12 arerotated about the rotational axis Z. A field of view (FOV) is set in anopening 19 of the rotation frame 13.

In the present embodiment, a rotational axis of the rotation frame 13 ina non-tilting state or a longitudinal direction of a table top 33-2 ofthe couch 30-2 is defined as a Z direction, a direction orthogonal tothe Z direction and horizontal to a floor surface is defined as an Xdirection, and a direction orthogonal to the Z direction andperpendicular to the floor surface is defined as a Y direction.

The X-ray high voltage apparatus 14 includes a high-voltage generatorand an X-ray controller. The high-voltage generator includes electricalcircuitry such as a transformer (trans) and a rectifier, and generates ahigh voltage to be applied to the X-ray tube 11 and a filament currentto be supplied to the X-ray tube 11. The X-ray controller controls thehigh-voltage to be applied to the X-ray tube 11 and the filament currentto be supplied to the X-ray tube 11. The high-voltage generator may be atransformer type or may be an inverter type. The X-ray high-voltageapparatus 14 may be provided on the rotation frame 13 inside the gantry10-2, or may be provided on a fixed frame (not shown) inside the gantry10-2.

The wedge filter 16 adjusts the dose of X-ray to be irradiated on thesubject P. Specifically, the wedge filter 16 attenuates the X-ray sothat the X-ray dose to be irradiated on the subject P from the X-raytube 11 has a predetermined distribution. For example, as the wedgefilter 16, a metal filter formed by processing metal such as aluminum isused. The wedge filter 16 is processed to have a predetermined targetangle or a predetermined thickness. The wedge filter 16 is also referredto as a bow-tie filter.

The collimator 17 limits an irradiation range of the X-ray passingthrough the wedge filter 16. The collimator 17 slidably supports aplurality of lead plates shielding the X-ray, and adjusts the shape ofslit formed by the plurality of lead plates. The collimator 17 is alsoreferred to as an X-ray diaphragm.

The DAS 18 reads an electric signal in accordance with the X-ray dosedetected by the X-ray detector 12 from the X-ray detector 12, amplifiesthe read electric signal, and integrates the electric signal over a viewperiod to acquire projection data having a digital value in accordancewith the X-ray dose over the view period. The DAS 18 is realized by, forexample, ASIC which is equipped with a circuit element that is capableof generating projection data. The projection data generated by the DAS18 is transmitted from a transmitter having a light-emitting diode (LED)provided on the rotation frame 13 to a receiver having a light-emittingdiode (LED) provided on a non-rotating portion (for example, a fixedframe) of the gantry 10-2 by optical communication, and is transmittedfrom the receiver to the medical data processing apparatus 50-2. Thetransmission method of the projection data from the rotation frame 13 tothe non-rotating portion of the gantry 10-2 is not limited to theaforementioned optical communication, and may be any method as long asit is a non-contact type data transmission.

The couch 30-2 comprises a base 31-2 and the table top 33-2. The base31-2 is installed on a floor surface. The base 31-2 has a structure thatsupports a support frame movably in a direction (Y direction)perpendicular to the floor surface. The support frame is a frameprovided on an upper part of the base 31-2. The support frame supportsthe table top 33-2 slidably along a center axis Z. The table top 33-2has a flexible plate-like structure on which the subject P is placed. Acouch motor is accommodated in the couch 30-2. The couch motor is amotor or an actuator that generates power for moving the table top 33-2on which the subject P is placed. The couch motor operates under thecontrol of the controller 15 or the medical data processing apparatus50-2, etc.

The controller 15 controls the X-ray high voltage apparatus 14, the DAS18, and the couch 30-2 in order to execute X-ray CT imaging inaccordance with an imaging control performed by processing circuitry 51.The controller 15 includes processing circuitry including a CPU, etc.,and a drive apparatus, such as a motor and an actuator. The processingcircuitry includes a processor, such as a CPU, and a memory, such as ROMand RAM, as hardware resources. The controller 15 controls the gantry10-2 and the couch 30-2 in accordance with, for example, a controlsignal received from an input interface 54 provided on the medical dataprocessing apparatus 50-2, the gantry 10-2, and the couch 30-2, etc. Forexample, the controller 15 controls rotation of the rotation frame 13,tilt of the gantry 10-2, and operations of the table top 33-2 and thecouch 30-2.

The medical data processing apparatus 50-2 shown in FIG. 11 is acomputer including the processing circuitry 51, a memory 52, a display53, the input interface 54, and a communication interface 55.

The processing circuitry 51 is, for example, a processor that functionsas a core of the X-ray computed tomography apparatus 1-2. The processingcircuitry 51 executes a program stored in the memory 52 to realize afunction corresponding to the program. The processing circuitry 51realizes, for example, an acquisition function 511, a generatingfunction 512, an output function 513, a display control function 514,and a training function 515. A1though not shown, the processingcircuitry 51 realizes an imaging control function, a reconfigurationprocessing function, an image processing function, a similaritycalculation function, and a weight changing function.

In the imaging control function, the processing circuitry 51 controlsthe X-ray high voltage apparatus 14, the controller 15, and the DAS 18to perform CT imaging. The processing circuitry 51 controls the X-rayhigh voltage apparatus 14, the controller 15, and the DAS 18 inaccordance with a user instruction via the input interface 54, or anautomatically set photographing condition (CT imaging parameter). The CTimaging parameter will be described later.

In the reconstruction processing function, the processing circuitry 51generates a CT image based on the projection data output from the DAS18. Specifically, the processing circuitry 51 performs preprocessingsuch as logarithmic conversion processing or offset correctionprocessing, sensitivity correction processing between channels, and beamhardening correction with respect to the projection data output from theDAS 18. The processing circuitry 51 then performs reconstructionprocessing with respect to the preprocessed projection data using afilter correction back projection method or an iterative approximationreconstruction method, etc., and generates the CT image.

In the image processing function, the processing circuitry 51 convertsthe CT image generated by the reconstruction processing function into atomogram of an arbitrary cross section or a three-dimensional imagebased on an input operation received from an operator via the inputinterface 54.

Explanations on the memory 52, the display 53, the input interface 54,the communication interface 55, the acquisition function 511, thegenerating function 512, the output function 513, the display controlfunction 514, and the training function 515 will be omitted.Furthermore, the similarity calculation function and the weight changingfunction will be described later.

Hereinafter, an operation example of the X-ray computed tomographyapparatus 1-2 according to the present embodiment will be explained.

FIG. 12 shows a first example of input/output of a trained model used inthe generating function of FIG. 11, and a CT imaging apparatus of anoutput destination. By the acquisition function 511, the processingcircuitry 51 acquires CT image data. By the generating function 512, theprocessing circuitry 51 applies a trained model 69 to the CT image dataand generates a CT imaging parameter relating to the CT image data. Bythe output function 513, the processing circuitry 51 outputs the CTimaging parameter to a CT imaging apparatus 70.

The CT imaging parameter is a parameter relating to imaging of an X-raycomputed tomography apparatus when acquiring the CT image data.Furthermore, the CT imaging parameter may include information relatingto the X-ray computed tomography apparatus.

Examples of the CT imaging parameter include a dose, a parameter ofiterative approximation processing, a parameter of AI reconstructionprocessing, FOV, a matrix size, dual energy, window level/window width(WL/WW), types of acquisition bins, or information on the number of rowsof multi-slices.

The dose includes, for example, information on the value of the tubecurrent and information on the value of the tube voltage. The parameterof iterative approximation processing includes, for example, informationon the type of iterative approximation processing (for example, anexpectation maximization (EM) method and an algebraic reconstructiontechnique (ART) method) and information on a blend rate. The blend rateindicates a composition ratio between an initial image and an updatedimage used in the iterative approximation processing. The parameter ofAI reconstruction processing includes, for example, information relatingto a noise reduction rate. The dual energy includes, for example,information on whether or not dual energy is used.

FIG. 13 shows a second example of input/output of a trained model usedin the generating function of FIG. 12, a CT imaging apparatus of anoutput destination, and a specific example of processing relating toinput. In the example of FIG. 13, display of a CT image generated inreal time can be optimized by generating a CT imaging parameter based onCT image-like image data to be of reference, executing imaging based onthe generated CT image parameter, and feeding back information on CTimage data generated by the imaging to the generating processing of theCT imaging parameter. Summarizing FIG. 13, the X-ray computed tomographyapparatus 1-2 is capable of performing feedback control in real time forthe display of the CT image by using the CT image data generated by theCT imaging.

It should be noted that, in FIG. 13, although the CT image-like imagedata is input to a trained model 71, the data is not limited thereto,and may be the CT image data. Furthermore, the trained model 71 of FIG.13 is capable of changing the weight of the model based on score data,and varying each value of the CT imaging parameter.

FIG. 14 is a flowchart for explaining a series of events including CTimaging parameter estimation processing in the second embodimentrelating to FIG. 13. For example, the flowchart of FIG. 14 starts by theprocessing circuitry 51 executing a CT imaging parameter estimationprogram, which is triggered by an instruction to activate an applicationrelating to the CT imaging parameter estimation processing input by auser.

(Step SB1)

When the CT imaging parameter estimation program is executed, theprocessing circuitry 51 executes the acquisition function 511. When theacquisition function 511 is executed, the processing circuitry 51acquires CT image-like image data assigned by the user.

(Step SB2)

After acquiring the CT image-like image data, the processing circuitry51 executes the generating function 512. When the generating function512 is executed, the processing circuitry 51 applies the trained model71 to the CT image-like image data and generates a CT imaging parameterrelating to the CT image-like image data.

(Step SB3)

After generating the CT imaging parameter, the processing circuitry 51executes the output function 513. When the output function 513 isexecuted, the processing circuitry 51 outputs the CT imaging parameterto a CT imaging apparatus 72. The CT imaging apparatus 72 will beexplained as being identical to the X-ray computed tomography apparatus1-2.

(Step SB4)

After receiving the CT imaging parameter, the processing circuitry 51executes the imaging control function. When the imaging control functionis executed, the processing circuitry 51 executes CT imaging using theCT imaging parameter generated in step SB2, and acquires projectiondata.

(Step SB5)

After acquiring the projection data, the processing circuitry 51executes the reconstruction processing function. When the reconstructionprocessing function is executed, the processing circuitry 51reconstructs the acquired projection data and generates CT image data.

(Step SB6)

After generating the CT image data, the processing circuitry 51 executesthe display control function 514. When the display control function 514is executed, the processing circuitry 51 displays the generated CT imagedata on the display 53.

(Step SB7)

After the CT image data is displayed, the processing circuitry 51executes the similarity calculation function. When the similaritycalculation function is executed, the processing circuitry 51 calculatesthe similarity between the CT image-like image data acquired in step SB1and the generated CT image data. For example, the similarity is obtainedbased on a feature amount that is calculated for each image data.

(Step SB8)

After the similarity is calculated, the processing circuitry 51determines whether or not the calculated similarity is higher than athreshold value. In the case where the similarity is higher than thethreshold value, it is determined that the display of the CT imagegenerated in real time is optimized, and the processing is ended. In thecase where the similarity is equal to or lower than the threshold value,it is determined that the display of the CT image generated in real timeis not optimized, and the processing proceeds to step SB9.

(Step SB9)

After performing the processing relating to the determination of thesimilarity, the processing circuitry 51 executes the weight changingfunction. When the weight changing function is executed, the processingcircuitry 51 changes the weight of the trained model 71 based on scoredata corresponding to the similarity. After step SB9, the processingreturns to step SB2.

In the flowchart of FIG. 14, an example of optimizing the display of theCT image generated in real time has been explained; however, this is notlimited to real time. For example, the CT imaging may be executed onlyby the initially estimated CT imaging parameter, and the CT imagingparameter estimated after the second estimation may be used foroptimizing the display screen of the generated CT image. Specifically,the processing circuitry 51 optimizes the display screen of the CT imagedata by using WL/WW included in the CT imaging parameter estimated afterthe second estimation.

As explained above, the X-ray computed tomography apparatus according tothe second embodiment acquires CT image data, and generates a CT imagingparameter by inputting the CT image data to a trained model, the CTimaging parameter being a parameter of the X-ray computed tomographyapparatus with respect to the CT image data, the trained model beingtrained to generate a CT imaging parameter of the X-ray computedtomography apparatus based on a CT image data. Therefore, the X-raycomputed tomography apparatus according to the present embodiment iscapable of estimating a CT imaging parameter used for CT imaging from CTimage data of which the CT imaging parameter is unknown so that, forexample, a user may set an optimal CT imaging parameter.

Third Embodiment

In the second embodiment, the medical data processing apparatus isexplained as being a computer mounted on the X-ray computed tomographyapparatus. On the other hand, in the third embodiment, the medical dataprocessing apparatus will be explained as being a computer mounted on anultrasound diagnostic apparatus. In the following explanation,constituent elements having functions almost identical to those in thefirst embodiment will be given identical symbols, and will be providedwith explanations only when necessary.

FIG. 15 shows a configuration of the ultrasound diagnostic apparatusaccording to the third embodiment. As shown in FIG. 15, an ultrasounddiagnostic apparatus 1-3 includes an ultrasound probe 10-3 and a medicaldata processing apparatus (apparatus main body) 50-3.

For example, the ultrasound probe 10-3 executes ultrasound scanning fora scanning region inside a living body of a patient, etc. in accordancewith a control of the medical data processing apparatus 50-3. Theultrasound probe 10-3 includes, for example, a plurality ofpiezoelectric vibrators, a matching layer, and a backing material. Inthe present embodiment, the ultrasound probe 10-3 includes, for example,a plurality of piezoelectric vibrators arranged along a predetermineddirection. The ultrasound probe 10-3 is detachably connected to themedical data processing apparatus 50-3.

The plurality of piezoelectric vibrators generate ultrasound inaccordance with a drive signal supplied from ultrasound transmittingcircuitry 57 included in the medical data processing apparatus 50-3.This allows the ultrasound probe 10-3 to transmit ultrasound to theliving body. When the ultrasound is transmitted to the living body fromthe ultrasound probe 10-3, the transmitted ultrasound waves arecontinuously reflected on a discontinuous surface having an acousticimpedance in a tissue of the living body, and is received by a pluralityof piezoelectric vibrators as a reflected wave signal. An amplitude ofthe received reflected wave signal depends on an acoustic impedancedifference at the discontinuous surface on which the ultrasound isreflected. Furthermore, a reflected wave signal obtained in the casewhere a transmitted ultrasound pulse is reflected on a movingbloodstream or a surface of a radiation-absorbency tissue spacer, etc.depends on a velocity component in an ultrasound transmission directionof a moving body and is frequency shifted due to the Doppler effect. Theultrasound probe 10-3 receives the reflected wave signal from the livingbody and converts it into an electric signal. The electric signal issupplied to the medical data processing apparatus 50-3.

The medical data processing apparatus 50-3 show in FIG. 15 is a computerfor generating and displaying an ultrasound image based on the reflectedwave signal received by the ultrasound probe 10-3. As shown in FIG. 15,the medical data processing apparatus 50-3 includes the ultrasoundtransmitting circuitry 57, ultrasound receiving circuitry 56, processingcircuitry 51, a memory 52, a display 53, an input interface 54, and acommunication interface 55.

The ultrasound transmitting circuitry 57 is a processor for supplying adrive signal to the ultrasound probe 10-3. The ultrasound transmittingcircuitry 57 is executed by, for example, trigger generating circuitry,delay circuitry, and pulsar circuitry. The trigger generating circuitryrepeatedly generates a rate pulse for forming transmission ultrasound bya predetermined rate frequency. The delay circuitry provides each ratepulse generated by the trigger generating circuitry with a delay timefor each piezoelectric vibrator necessary for converging the ultrasoundgenerated by the ultrasound probe 10-3 into a beam and determiningtransmission directivity. At a timing based on the rate pulse, thepulsar circuitry applies the drive signal (drive pulse) to a pluralityof ultrasound vibrators provided on the ultrasound probe 10-3. Byarbitrarily varying the delay time to be provided to each rate pulse bythe delay circuitry, a transmission direction from a piezoelectricvibrator surface is arbitrarily adjusted.

The ultrasound receiving circuitry 56 is a processor that performsvarious types of processing with respect to the reflected wave signalreceived by the ultrasound probe 10-3 and generates a reception signal.The ultrasound receiving circuitry 56 is realized by, for example,amplifier circuitry, an A/D converter, reception delay circuitry, and anadder. The amplifier circuitry amplifies the reflected wave signalreceived by the ultrasound probe 10-3 for each channel, and performsgain correction processing. The A/D converter converts the reflectedwave signal to which gain correction is applied into a digital signal.The reception delay circuitry provides the digital signal with a delaytime necessary for determining reception directivity. The adder adds aplurality of digital signals to which the delay time is given. By theadding processing performed by the adder, a reception signal in which areflection component is emphasized from a direction in accordance withthe reception directivity is generated.

The processing circuitry 51 is a processor that functions, for example,as a core of the ultrasound diagnostic apparatus 1-3. The processorcircuitry 51 executes a program stored in the memory 52 to realize afunction corresponding to the program. The processing circuitry 51includes, for example, an acquisition function 511, a generatingfunction 512, an output function 513, a display control function 514,and a training function 515. A1though not shown, it should be noted thatthe processing circuitry 51 includes a B-mode processing function, aDoppler mode processing function, an image processing function, asimilarity calculation function, and a weight changing function.

In the B-mode processing function, the processing circuitry 51 generatesB-mode data based on the reception signal received from the ultrasoundreceiving circuitry 56. Specifically, for example, the processingcircuitry 51 performs envelope detection processing and logarithmicamplification processing, etc. on the reception signal received from theultrasound receiving circuitry 56, and generates data (B-mode data) inwhich signal strength is expressed by the brightness of luminance. Thegenerated B-mode data is stored in a RAW data memory (not shown) as dataon a two-dimensional ultrasound scanning line (raster).

In the Doppler-mode processing function, the processing circuitry 51performs frequency analysis on the reception signal received from theultrasound receiving circuitry 56 to generate data (Doppler data) inwhich motion information based on the Doppler effect of a bloodstream ina region of interest (ROI) set in a scan region is extracted.Specifically, the processing circuitry 51 generates Doppler data inwhich an average velocity, an average dispersion value, and averagepower, etc. at each of a plurality of sample points are estimated as themotion information of the bloodstream. The generated Doppler data isstored in the RAW data memory (not shown) as data on the two-dimensionalultrasound scanning line.

Furthermore, in the Doppler mode processing function, the processingcircuitry 51 is capable of executing a color Doppler method which isreferred to as a color flow mapping (CFM) method. In the CFM method,ultrasound transmission/reception is performed a number of times on aplurality of scanning lines. By applying a moving target indicator (MTI)filter with respect to data rows of the same position, the processingcircuitry. 51 suppresses a signal (clutter signal) derived from astationary tissue or a slow moving tissue, and extracts a signal derivedfrom a bloodstream. The processing circuitry 51 then estimatesinformation on the velocity, the dispersion, or the power, etc. of thebloodstream from the extracted signal.

In the image processing function, the processing circuitry 51 generatesvarious types of ultrasound image data based on data generated by theB-mode processing function and the Doppler processing function.Specifically, the processing circuitry 51 generates B-mode image dataconfigured by pixels by executing, for example, a RAW-pixel conversionwith respect to B-mode RAW data stored in the RAW data memory, which is,for example, executing coordinate conversion in accordance with anultrasound scanning mode by the ultrasound probe 10-3.

Furthermore, in the image processing function, the processing circuitry51 executes, for example, image processing, such as RAW-pixelconversion, with respect to Doppler RAW data stored in the RAW datamemory to generate Doppler image data in which bloodstream informationis visualized. The Doppler image data is velocity image data, dispersionimage data, power image data, or image data obtained by combinationsthereof. It should be noted that the Doppler image data generated fromDoppler data acquired by the color Doppler method may be referred to ascolor Doppler ultrasound image data.

The explanations on the memory 52, the display 53, the input interface54, the communication interface 55, the acquisition function 511, thegenerating function 512, the output function 513, the display controlfunction 514, and the training function 515 will be omitted.Furthermore, the similarity calculation function and the weight changingfunction will be described later.

Hereinafter, the ultrasound diagnostic apparatus 1-3 according to thepresent embodiment will be explained.

FIG. 16 shows a first example of input/output of a trained model used inthe generating function of FIG. 15, and an ultrasound diagnosticapparatus of an output destination. The processing circuitry 51 acquiresultrasound image data by the acquisition function 511. By the generatingfunction 512, the processing circuitry 51 applies a trained model 73 tothe ultrasound image data and generates an ultrasound imaging parameterrelating to the ultrasound data. By the output function 513, theprocessing circuitry 51 outputs the ultrasound imaging parameter to anultrasound imaging apparatus 74.

The ultrasound imaging parameter is a parameter relating to imaging ofthe ultrasound diagnostic apparatus when acquiring the ultrasound imagedata. Furthermore, the ultrasound imaging parameter may includeinformation relating to the ultrasound diagnostic apparatus.

Examples of the ultrasound imaging parameter include the type of probe,a frame rate, a dynamic range, a frequency, and a flow velocity range.The frequency includes, for example, information on a center frequencyof a transmission signal. The flow velocity range includes, for example,information on a flow velocity range in a color Doppler display.

FIG. 17 shows a second example of input/output of the trained model usedin the generating function of FIG. 16, an ultrasound diagnosticapparatus of an output destination, and .a specific example ofprocessing relating to input. In the example of FIG. 17, a display of anultrasound image generated in real time can be optimized by generatingan ultrasound imaging parameter based on color Doppler ultrasoundimage-like image data to be of reference, executing imaging based on thegenerated ultrasound imaging parameter, and feeding back information onultrasound image data generated by the imaging to the generatingprocessing of the ultrasound imaging parameter. Summarizing FIG. 17, theultrasound diagnostic apparatus 1-3 is capable of performing feedbackcontrol in real time for the display of the ultrasound image by usingthe ultrasound image data generated by ultrasound imaging.

It should be noted that, in FIG. 17, although the color Dopplerultrasound image-like image data is input to a trained model 75, thedata is not limited thereto, and may be the color Doppler ultrasoundimage data. Furthermore, the trained model 75 of FIG. 17 is capable ofchanging the weight of the model based on score data, and varying eachvalue of the ultrasound imaging parameter.

FIG. 18 is a flowchart for explaining a series of events includingultrasound imaging parameter estimation processing in the thirdembodiment relating to FIG. 17. For example, the flowchart of FIG. 18starts by the processing circuitry 51 executing an ultrasound imagingparameter estimation program, which is triggered by an instruction toactivate an application relating to the ultrasound imaging parameterestimation processing input by a user.

(Step SC1)

When the ultrasound imaging parameter estimation program is executed,the processing circuitry 51 executes the acquisition function 511. Whenthe acquisition function 511 is executed, the processing circuitry 51acquires color Doppler ultrasound image-like image data assigned by theuser.

(Step SC2)

After acquiring the color Doppler ultrasound image-like image data, theprocessing circuitry 51 executes the generating function 512. When thegenerating function 512 is executed, the processing circuitry 51 appliesthe trained model 75 to the color Doppler ultrasound image-like imagedata and generates an ultrasound imaging parameter relating to the colorDoppler ultrasound image-like image data. It should be noted that thisultrasound imaging parameter includes the flow velocity range.

(Step SC3)

After generating the ultrasound imaging parameter, the processingcircuitry 51 executes the output function 513. When the output function513 is executed, the processing circuitry 51 outputs the ultrasoundimaging parameter to an ultrasound imaging apparatus 76. The ultrasoundimaging apparatus 76 to be explained is assumed to be identical to theultrasound diagnostic apparatus 1-3.

(Step SC4)

After receiving the ultrasound imaging parameter, the processingcircuitry 51 executes the imaging control function. When the imagingcontrol function is executed, the processing circuitry 51 executesultrasound imaging using the ultrasound imaging parameter generated instep SC2, and acquires a reception signal.

(Step SC5)

After acquiring the reception signal, the processing circuitry 51executes the Doppler mode processing function. When the Doppler modeprocessing function is executed, the processing circuitry 51 generatesDoppler data by performing frequency analysis on the acquired receptionsignal.

(Step SC6)

After generating the Doppler data, the processing circuitry 51 executesthe image processing function. When the image processing function isexecuted, the processing circuitry 51 generates color Doppler ultrasoundimage data by performing image processing on the generated Doppler data.

(Step SC7)

After the color Doppler ultrasound image data is generated, theprocessing circuitry 51 executes the display control function 514. Whenthe display control function 514 is executed, the processing circuitry51 displays the generated color Doppler ultrasound image data on thedisplay 53. Furthermore, the processing circuitry 51 displays the colorDoppler ultrasound image data based on the flow velocity range includedin the ultrasound imaging parameter.

(Step SC8)

After the color Doppler ultrasound image data is displayed, theprocessing circuitry 51 executes the similarity calculation function.When the similarity calculation function is executed, the processingcircuitry 51 calculates the similarity between the color Dopplerultrasound image-like image data acquired in step SC1 and the generatedcolor Doppler ultrasound image data.

(Step SC9)

After the similarity is calculated, the processing circuitry 51determines whether or not the calculated similarity is higher than athreshold value. In the case where the similarity is higher than thethreshold value, it is determined that the display of a color Dopplerultrasound image generated in real time is optimized, and the processingis ended. In the case where the similarity is equal to or lower than thethreshold value, it is determined that the display of the color Dopplerultrasound image generated in real time is not optimized, and theprocessing proceeds to step SC10.

(Step SC10)

After performing the processing relating to the determination of thesimilarity, the processing circuitry 51 executes the weight changingfunction. When the weight changing function is executed, the processingcircuitry 51 changes the weight of the trained model 75 based on scoredata corresponding to the similarity. After step SC10, the processingreturns to step SC2.

As explained above, the ultrasound diagnostic apparatus according to thethird embodiment acquires ultrasound image data, and generates anultrasound imaging parameter by inputting the ultrasound image data to atrained model, the ultrasound imaging parameter being a parameter of theultrasound diagnostic apparatus with respect to the ultrasound imagedata, the trained model being trained to generate an ultrasound imagingparameter of the ultrasound diagnostic apparatus based on an ultrasoundimage data. Therefore, the ultrasound diagnostic apparatus according tothe present embodiment is capable of estimating an ultrasound imagingparameter used for ultrasound imaging from ultrasound image data ofwhich the ultrasound imaging parameter is unknown so that, for example,a user may set an optimal ultrasound imaging parameter.

Various types of imaging parameter estimation can be used for performingindexing processing with respect to medical data. As an example, a statein which a plurality of pieces of medical data are stored in a storage(for example, SSD, HDD, or a cloud storage) is considered. If themedical data is data that is imaged by a variety of imaging parameters,a user's request to search for only the related medical data inaccordance with the purpose thereof may often occur. In order toefficiently refer to such medical data, processing referred to asindexing is often performed to add additional information such as animaging parameter to the medical data in advance. By adding a parameterthat is estimated using the imaging parameter estimation to the medicaldata held in the storage, when the user performs a search request, therelated medical data can be output while referring to the estimationparameter. Furthermore, by performing indexing with respect to an imageof a web site or a paper image, etc., using various types of imagingparameter estimation, a massive search of the related medical data wouldalso be possible.

In addition, by using various types of imaging parameter estimation withrespect to medical data that is not in DICOM format, such as an indexingWeb site image or paper image, and outputting the result thereof as theDICOM format, the result can be captured in searchable form in a workstation having a search function corresponding to the medical data inDICOM format.

According to at least one of the embodiments explained above, an optimalimaging parameter can be set.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

1. A medical data processing apparatus, comprising processing circuitryconfigured to: acquire medical data; and generate an imaging parameterby inputting the medical data to a trained model, the imaging parameterbeing a parameter of a medical image diagnostic apparatus with respectto the medical data, the trained model being trained to generate animaging parameter of the medical image diagnostic apparatus based onmedical data.
 2. The medical data processing apparatus according toclaim 1, wherein the medical data includes medical image data, andwherein the processing circuitry is further configured to generate theimaging parameter by inputting the medical image data to the trainedmodel, the imaging parameter being related to the medical image data,the trained model being trained to generate an imaging parameter basedon medical image data.
 3. The medical data processing apparatusaccording to claim 1, wherein the medical data includes medical imagedata, and supplementary data including a part of a parameter relating tothe medical image data, wherein the processing circuitry is furtherconfigured to generate the imaging parameter by inputting the medicalimage data and the supplementary data to the trained model, the imagingparameter being related to the medical image data, the trained modelbeing trained to generate an imaging parameter based on medical imagedata and supplementary data.
 4. The medical data processing apparatusaccording to claim 3, wherein the supplementary data is digital imagingand communication in medicine (DICOM) data.
 5. The medical dataprocessing apparatus according to claim 1, wherein the medical dataincludes medical image data and label data including informationrelating to a type of the medical image data, wherein the processingcircuitry is further configured to generate the imaging parameter byinputting the medical image data and the label data to the trainedmodel, the imaging parameter being related to the medical image data,the trained model being trained to generate an imaging parameter basedon medical image data and label data.
 6. The medical data processingapparatus according to claim 2, wherein the medical image data isassociated with the medical image diagnostic apparatus.
 7. The medicaldata processing apparatus according to claim 1, wherein the medical dataincludes medical image-like image data obtained by photographing orreading a medical image printed on paper or recorded on film.
 8. Themedical data processing apparatus according to claim 7, wherein theimage data is associated with the medical image diagnostic apparatus. 9.The medical data processing apparatus according to claim 2, wherein themedical image data is magnetic resonance image data, and the imagingparameter is a parameter relating to a magnetic resonance imagingapparatus.
 10. The medical data processing apparatus according to claim9, wherein the imaging parameter includes a sequence of the magneticresonance imaging apparatus.
 11. The medical data processing apparatusaccording to claim 1, wherein the medical data includes map image datagenerated based on a plurality of pieces of medical image data, whereinthe processing circuitry is further configured to generate a pluralityof imaging parameters by inputting the map image data to the trainedmodel, the plurality of imaging parameters being related to theplurality of pieces of medical image data, the trained model beingtrained to generate a plurality of imaging parameters based on map imagedata generated based on a plurality of pieces of medical image data. 12.A medical image diagnostic apparatus, comprising: the medical dataprocessing apparatus according to claim 1; and an imaging apparatusconfigured to perform medical imaging based on the imaging parameter.13. The medical image diagnostic apparatus according to claim 12,further comprising control circuitry configured to perform feedbackcontrol in real time by using medical data generated by the medicalimaging.